Microcalcifications(MCs) are tiny spots of calcium deposits that can be scattered or clustered throughout the tissue as found in 2D and 3D images. Singled out MCs indicate the presence of tiny benign cysts, but their clustered appearance signify the presence of early cancer. When found on a mammogram, MCs simply indicate the presence of early breast cancer. The early delectation of MC will aid in reducing the rate of mortality in women. Hence, an adequate and robust MC detection technique is necessary to increase the performance of breast cancer diagnosis. However, identification of MC had been a challenging task to the researchers. This is due to a large variety of breast composition, highly textured breast anatomy and inherent low contrast of mammogram in spite of the recent advances of technology. Moreover MC is very small in size. In mammograms, they appear as a tiny bright spots. Along with MC, different types of breast structures (like curvilinear structures) may appear. It increases the chance of false positives in MC detection technique.
In recent periods, researchers have tried different image processing, machine learning as well as statistical approaches to single out MCs in mammogram image. Nishikawa et al. (1995) introduced a difference technique to enhance MC followed by morphological erosion to reduce false positives. In difference technique approach two consecutive filters are introduced; one for enhancing the MCs and the later for suppressing them [Nishikawa, R. M., Giger, M. L., K. D. C. J. V., and Schmidt, R. A., “Computer-aided detection of clustered micro-calcifications on digital mammograms”, Medical and Biological Engineering and Computing, Springer Berlin/Heidelberg, 1994, pp. 174-178]. Chang et al. (1998) proposed a fuzzy logic based MC detection approach followed by curvilinear structure removal using curve detector to reduce false positive percentages [Cheng, H, Y. M. L., and Freimanis, R. I., “A Novel Approach to Micro-calcification Detection Using Fuzzy Logic Technique,” IEEE Transactions on Medical Imaging vol. 17, issue 3, 1998, pp. 442-450]. Gurcan et al. (1999) presented nonlinear sub band decomposition technique for MC detection [Gurcan, M. N., Y. Y. and cetin, A. E., “Micro-calcification segmentation and mammogram image enhancement using nonlinear filtering”, Proceedings of the IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing (NSIP'99), Antalya, Turkey, 1999, June 20-23]. Melloul et al. (2002) developed an entropy thresholding based approach for MC detection. In this two stage technique, multi-scale morphological operation is initially introduced for removing background tissue followed by entropy thresholding based on 3D co-occurrence matrix for locating MCs [Melloul, M. and Joskowicz, L. “Segmentation of micro-calcification in X-ray mammograms using entropy thresholding”, Technical report, In Proceedings of the 16th International Congress on Computer-Assisted Radiology and Surgery, 2002]. Naga et al. (2002) demonstrated that classification efficiency could be farther improved by applying successive enhancement learning based support vector machine approach (SVM-SEL) [Naga, I., Yang, Y., Wernick, M. N., Galatsanos, N. P., and Nishikawa, R. M., “A Support Vector Machine Approach for Detection of Microcalcifications”, IEEE Trans. on Med. Imaging, vol. 21, December 2002, pp. 1552- 1563]. Guan et al. (2008) introduced scale invariant feature transform (SIFT) to locate MCs [Guan, Q., Zhang, J., S. C. and Todd-Pokropeka, A., “Automatic Segmentation of Micro-calcification Based on SIFT in Mammograms”, BMEI ‘08: Proceedings of the 2008 International Conference on Biomedical Engineering and Informatics’, IEEE Computer Society, Washington, DC, USA, 2008, pp. 13-17]. Normalized Tsallis entropy and Type II fuzzy set technique has been introduced by Mohanalin et al (2009) in MC detection [Mohanalin, J., P. K. K. and Kumar, N., “Micro-calcification Segmentation Using Normalized Tsallis Entropy: An Automatic “q” Calculation by Exploiting Type II Fuzzy Sets,” IETE Journal of Research, vol. 55, 2009, pp. 90-96]. According to literature, the machine learning based MC detection method seems to achieve the best accuracy [Jinshan, T., Rangayyan, R. M., Jun, X., El Naga, I., and Yongyi, Y.; “Computer-Aided Detection and Diagnosis of Breast Cancer with Mammography: Recent Advances”, IEEE Trans. on Information Technology in Biomedicine, 2009, pp. 236-251].
Along with MC detection, different types of breast structures (like curvilinear structures) may appear as false positives. In literature, curve detector [2], phase congruency [Linguraru, M. G., Marias, K., Ruth, E., and Brady, M., “A biologically inspired algorithm for micro-calcification cluster detection”, Medical Image Analysis Elsevier, vol. 10, Issue 6, pp. 850-862, December 2006] etc. has been tried out for curvilinear structure removal. It enhances the quality of MC detection algorithm.
There has been thus a need in the art to developing a method and apparatus for MC detection in 2D and 3D with a background of large variety of breast composition with highly textured breast anatomy and inherent low contrast of mammogram, ensuring very high true positive(TP) and low false positive(FP) simultaneously in the detection process. In the present work, a novel and automated MC detection technique based on Nonlinear Energy
Operator (NEO) is presented after breast region segmentation. The same NEO output will help in curvilinear structure removal.